предыдущий|следующий

Accurate identification of inland wetland dynamic range under water level fluctuation

  • role： First author第一作者
  • Affiliation：

    College of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China

    Beijing Key Laboratory of Resources Environment and GIS, Beijing 100048, China

  • Email：luodaming33@163.com
  • Introduction：罗茗，研究方向为遥感技术在湿地领域的应用。E-mail：luodaming33@163.com
LUO Ming,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China

    Beijing Key Laboratory of Resources Environment and GIS, Beijing 100048, China

  • Email：gongzhn@cnu.edu.cn
  • Introduction：宫兆宁，研究方向为遥感技术及地学分析应用。E-mail：gongzhn@cnu.edu.cn
GONG Zhaoning*,  
  • Affiliation：

    College of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China

    Beijing Key Laboratory of Resources Environment and GIS, Beijing 100048, China

ZHANG Yuan

реферат

The accurate identification of the wetland dynamic range is the basis for the protection and restoration of the wetland ecosystem. To maximize the advantages of high timeliness and large-scale repeated observation of remote sensing technology and in consideration of the characteristics of high spatial heterogeneity and high temporal dynamics of wetlands, all Landsat OLI time series image datasets available for Google Earth Engine were used to study the accurate identification of inland wetland dynamic range under water level fluctuation.Three typical inland wetlands were selected as research areas on the basis of the genetic factors and the hydrological factors of wetlands. In combination with the diagnostic characteristics of mature wetlands, i.e., hygrophyte and wet soil, the combination of water-wetness indices for defining wetland scope was selected. Image composition was used to determine the high and low water levels in one year. A modified fuzzy C-means algorithm was proposed to reduce the spatial heterogeneity of wetland background and improve the contrast and distinction between wetland and nonwetland boundaries. The maximum between-class variance (OTSU) was selected to determine the adaptive threshold of wetland disinflation boundary and then combined with the superposition rules of the water-wetness index dynamic combination scheme to identify wetlands within one year. Finally, a set of accurate identification technology of wetland dynamic range based on the “Elements-Index-Threshold” technology System (EITS) was constructed.Typical inland wetlands, such as the Guanting Reservoir, the Zoige wetland, and the Poyang Lake wetland, were selected as experimental areas to verify the applicability and accuracy of the set of technology system. Results showed that the extraction accuracy of wetland range was higher than 94%, and the Kappa coefficient was greater than 0.88.This research improves the accuracy and efficiency of wetland spatiotemporal dynamic range identification, hoping to provide effective support for long-term and large-scale wetland dynamic monitoring and mapping.

ключеви́че слова́

remote sensing;defining the dynamic range of wetland;water-wetness index;dynamic process;water level fluctuation;time series data set;inland wetland

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